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XGBoost-Based Instantaneous Drowsiness Detection Framework Using Multitaper Spectral Information of Electroencephalography

Authors:

Sungroh YoonAuthors Info & Claims

BCB '18: Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics

Pages 111 - 121

https://doi.org/10.1145/3233547.3233567

Published: 15 August 2018 Publication History

Abstract

The socioeconomic losses caused by extreme daytime drowsiness are enormous in these days. Hence, building a virtuous cycle system is necessary to improve work efficiency and safety by monitoring instantaneous drowsiness that can be used in any environment. In this paper, we propose a novel framework to detect extreme drowsiness using a short time segment (~ 2 s) of EEG which well represents immediate activity changes depending on a person's arousal, drowsiness, and sleep state. To develop the framework, we use multitaper power spectral density (MPSD) for feature extraction along with extreme gradient boosting (XGBoost) as a machine learning classifier. In addition, we suggest a novel drowsiness labeling method by combining the advantages of the psychomotor vigilance task and the electrooculography technique. By experimental evaluation, we show that the adopted MPSD and XGB techniques outperform other techniques used in previous studies. Finally, we identify that spectral components (theta, alpha, and gamma) and channels (Fp1, Fp2, T3, T4, O1, and O2) play an important role in our drowsiness detection framework, which could be extended to mobile devices.

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XGBoost-Based Instantaneous Drowsiness Detection Framework Using Multitaper Spectral Information of Electroencephalography

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cover image ACM Conferences

BCB '18: Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics

August 2018

727 pages

ISBN:9781450357944

DOI:10.1145/3233547

General Chairs:
Amarda Shehu
George Mason University, USA
,
Cathy Wu
University of Delaware, USA
,
Program Chairs:
Christina Boucher
University of Florida, USA
,
Jing Li
Case Western Reserve University, USA
,
Hongfang Liu
Mayo Clinic, USA
,
Mihai Pop
University of Maryland, USA

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Published: 15 August 2018

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Brain Korea 21 Plus Project in 2018

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BCB '18: 9th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics

August 29 - September 1, 2018

DC, Washington, USA

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BCB '18 Paper Acceptance Rate 46 of 148 submissions, 31%;

Overall Acceptance Rate 254 of 885 submissions, 29%

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https://doi.org/10.1109/ICTEST60614.2024.10576142
Cruz MAbbade LLorenz PMafra SRodrigues J(2023)Detecting Compromised IoT Devices Through XGBoostIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2022.318725224:12(15392-15399)Online publication date: 1-Dec-2023
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